Interaction force measurement of parallel robots based on structure-integrated force sensors using interpretable linear neural networks

Abstract

The parallel robot in this research is mainly designed for the human-machine collaboration, which has two working modes: follow-up mode and pressing mode. In order to better realize these two functions, the measurement of the interaction force is mainly concerned. In the arrangement of the hardware, different from the six-dimensional force sensor method and current estimation method commonly used in industry, the structure-integrated force sensors (SIFS) measurement method is used. In the calculation method, a back propagation neural networks (BPNN) method is introduced to calculate the interaction force. Then, based on the prior knowledge of physics, a new interpretable linear neural networks (ILNN) method is proposed. The details of the ILNN algorithm and its differences from the classic neural network methods are given. Finally, the experimental results of the direct Newton–Euler method, the least square method, the BPNN method, and the ILNN method are compared. Experimental results show that the inference accuracy of the ILNN method is slightly better than the direct Newton–Euler method and the least square method, and its generalization ability is much better than the BPNN method. The use of the ILNN method based on the FIFS arrangement makes the measurement of the interaction force more accurate and reliable.